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Diabeat.us

Diabeat.us. Diabetes lifestyle management Andrew Alles Victor Benjamin Robert Erikson Xiao Liu. Introduction. With the emergence of web 2.0, individuals have been heavily using the Internet to share ideas and experiences

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Diabeat.us

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  1. Diabeat.us Diabetes lifestyle management Andrew Alles Victor Benjamin Robert Erikson Xiao Liu

  2. Introduction • With the emergence of web 2.0, individuals have been heavily using the Internet to share ideas and experiences • In particular, support groups are quite prominent and useful to Internet users, especially concerning medical problems • However, much of the content existing on the Internet today is disjointed • Requires users to visit multiple sources • Poor organization can make content hard to find

  3. Objectives • Diabeat.us seeks to advance the accessibility of diabetes related resources to Internet users • Diabetes related news and recipes • Educational books and videos • Web community analytics • Content represented must come from a set of diverse, yet credible information sources • Current leading diabetes information sources (e.g. diabetes.org) • Various popular web APIs contribute content and form • Information visualized from patient-generated web content

  4. What’s so special? • Functionalities • Meta-search across multiple renown diabetes resources • Nutritional Information, Recipes • News, Research Progress • Videos, Multimedia • Social media analytics • Sentiment analysis on potential diabetes treatments • Social network analysis to identify credible and helpful individuals in diabetes support communities

  5. Competitor Analysis • Much overlap exists between currently existing web communities • However, analysis of user generated content is not featured • Such analysis could benefit patients by quickly assessing the opinions and experiences of entire web communities. It gives us an edge where competitors cannot “diabeat” us.

  6. Business Models • Advertisement • Google Adsense • Amazon Referrals • Youtube Channel Partnership • Subscription-based content • Sentiment analysis of treatment discussions • Social network analysis of web communities

  7. Architectural Components • Web Design and Hosting • Amazon EC2 • Apache Tomcat • Windows Server 2008 • MSSQL 2008 • APIs • Twitter • Google Adsense, Feed, Search • Google Android • Amazon • Facebook • Youtube • Wikipedia • Flickr • IBM Many Eyes (SNA Visualization)

  8. Analytics and Novelty • We perform text mining of user-generated web content to quickly assess what people are saying about diabetes • Sentiment analysis on drug and treatments can quickly let patients know how others experience and feel various treatments

  9. Member Contributions • Member Contributions • Andrew Alles – Server Admin, App development • Victor Benjamin – Web and backend programming • Robert Erikson – API research and implementation • Xiao Liu – Analytics and API programming • All members assisted each other with various tasks • Each member participated in collection and extracting existing web content • Bug fixes and design changes handled collectively

  10. Future • Future • Advance analytic services and content • Expand website to include Chinese content • Development of analytics for Chinese content • Establish better social media presence

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